Stimulated vs. Non-stimulated MAIT Cells

# library(devtools)
# install_git("http://github.com/RGLab/MASTDataPackage")
# install_git('http://github.com/RGLab/MAST')
## Warning: replacing previous import by 'reshape::melt' when loading 'MAST'
## Warning: package 'S4Vectors' was built under R version 3.2.1

Hurdle model fitting

zlm <- zlm.SingleCellAssay(~condition + cngeneson, sca, method = "bayesglm", 
    ebayes = TRUE, ebayesControl = list(method = "MLE", model = "H1"))

Comparison of ranks with and without CDR effect.

On average, 35% of differentailly expressed genes are attributed only to a CDR effect.

Analysis of deviance for ngeneson (Supplementary Figure 2)

## Mean % Deviance
dataset component V1
mdc C 1.650106
mdc D 4.869932
MAIT C 4.135422
MAIT D 5.282912 
## % Deviance 90th Percentile
dataset component 90%
mdc C 4.392776
mdc D 9.364816
MAIT C 10.816661
MAIT D 13.380499

Visualization of most differentially expressed genes

Heatmap of MAITs based on most differentially expressed genes

Top 100 DE genes between stimulated and non-stimulated MAIT cells.

Some of the activated MAITs have transcriptional profiles more similar to unactivated MAITS.

GSEA

Heatmap and PCA of module scores

Figure 3 GSEA Results for MAIT Data Set

Figure 3

Figure 2 - Heatmap of Differentially Expressed Genes and Activated MAIT Module Scores

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Figure 4 - Residual Correlations and PCA

Supplementary Figure 10 - Change in residual background correlation after adjusting for CDR.

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egid chrmap symbol
1831 Xq22.3 TSC22D3
5716 Xq22.3 PSMD10
122704 14q11.2 MRPL52
3002 14q11.2 GZMB
51102 1p35.3 MECR
93974 1p35.3 ATPIF1

Supplementary Figure 14 - Heatmaps of DE genes between non-responding stimulated cells and stimulated / non-stimulated cells

GSEA between non-stimulated and non-responding cells

PCA plots for ngeneson effect

Figure 1

## [1] 5.956767 3.843590

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Flow cytometry data

## mac version of flowJo workspace recognized.
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